Team D: AI and Data Economy

Caitlin Clemmow, Yiqiao Chen, Lewis Jones, Michael Stevens and Alex Stringer

August 23, 2019

Mission: “Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030”

The problem

Obesity and Greenspace

“Research suggests we need a strategic approach to the provision of parks and green spaces by identifying areas where investment will have the most significant impact on individuals.” A quote from Fields in trust who also estimated parks & green spaces are estimated to save the NHS £111 million/year.

How will we address this mission?

We aim to address this by…

Our research question

Which districts in England should be priortised for targeted intervention against obesity?

Stakeholders:

  1. Governmental:
    • Department of health and social care
    • Public Health england
    • NHS England and NHS Improvement
    • Department of Digital, Culture, Media and Sport
    • UK Sport, Sport England
  2. Academics:
    • Academic Health Science Centre
  3. Organisations:
    • Health Data Research UK
  4. Healthcare users:
    • Patients, family, carers
  5. Sport & health related policies:
    • Everybody active, every day

Datasets

We downloaded this data from public health england via fingertipsR

Data Availability

We used the finest scale available to us (Local authority district level) whilst also conforming to the NHS’s requirement of anonymity.

Analysis, theory and code

We used a general linearised geostatistical model originally developed in Besag, York, and Mollié (1991), and elaborated on in Brown (2015). It shares similarities with a Poisson regression model but properly harnesses spatial relationships. All code is available on slack/github.

Results

## Warning: namespace 'INLA' is not available and has been replaced ## by .GlobalEnv when processing object 'obesity_model'

Discussion

General Recommendation: A geographically-targeted whole system approach

Why is this a good idea?

What are the caveats?

Data recommendations & Future research:

Ethics :

Although we identified the potential targeting areas that need interventions, the policy recommendation was made without the reinforcement of health inequalities. The further interventions using data-driven approach needs to protect the public’s right and privacy.

References

Bedimo-Rung, Ariane L. 2005. “The significance of parks to physical activity and public health: a conceptual model.” American Journal of Preventive Medicine 28 (2S2): 159–68. doi:10.1016/j.ampre.2004.10.024.

Besag, J, J York, and A Mollié. 1991. “A Bayesian image restoration with two applications in spatial statistics Ann Inst Statist Math 43: 1–59.” Find This Article Online 43 (1): 1–20.

Brown, Patrick E. 2015. “Model-based geostatistics the easy way.” Journal of Statistical Software 63 (12). doi:10.18637/jss.v063.i12.

Huang, R., A. V. Moudon, A. J. Cook, and A. Drewnowski. 2015. “The spatial clustering of obesity: Does the built environment matter?” Journal of Human Nutrition and Dietetics 28 (6): 604–12. doi:10.1111/jhn.12279.

Jean-louis, Sabine. 2018. “The Effects of Access to Green Space on Obesity: An Integrative Review.” Pediatrics 141 (1 MeetingAbstract): 221 LP–221. doi:10.1542/peds.141.1_MeetingAbstract.221.

Lachowycz, K., and A. P. Jones. 2011. “Greenspace and obesity: A systematic review of the evidence.” Obesity Reviews 12 (501): 183–89. doi:10.1111/j.1467-789X.2010.00827.x.

Lee, A. C.K., and R. Maheswaran. 2011. “The health benefits of urban green spaces: A review of the evidence.” Journal of Public Health 33 (2): 212–22. doi:10.1093/pubmed/fdq068.